{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Fashion_MNIST.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "rrmv005ylQ7S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Import the libraries\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gtXkPLVolXTH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        },
        "outputId": "50180478-4845-4117-9e09-6ba2f0372b53"
      },
      "source": [
        "#Load the data set\n",
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5wtRIgYnoG-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "943670f4-fc65-4591-9cff-7da008c646a2"
      },
      "source": [
        "#View a training image\n",
        "img_index = 2 # <<<<<  You can update this value to look at other images\n",
        "img = train_images[img_index]\n",
        "print(\"Image Label: \" + str(train_labels[img_index]))\n",
        "plt.imshow(img)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Image Label: 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f11aad769e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfiAowg8ERfiBoP4fZgOvGao0W+kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FChIqyqFWYgn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "3a9048fc-55f0-413b-c44e-178396e8a2d4"
      },
      "source": [
        "#Print the shape \n",
        "print(train_images.shape)# 60,000 rows of 28 x 28 pixel images\n",
        "print(test_images.shape) # 10,000 rows of 28 x 28 pixel images"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n",
            "(10000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZDAJt8I1ln9e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create the neural network model\n",
        "model = keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sFG6Mn_mGge",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Compile the model\n",
        "model.compile(optimizer=tf.train.AdamOptimizer(), loss='sparse_categorical_crossentropy', metrics = ['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96jJstpqmWkO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "f79a474e-c19f-4487-ef02-ef9cc1f33015"
      },
      "source": [
        "#Train the model\n",
        "model.fit(train_images, train_labels, epochs=5, batch_size=32)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.5249 - acc: 0.8066\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 0.5174 - acc: 0.8088\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 0.5132 - acc: 0.8102\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 0.5259 - acc: 0.8074\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.5226 - acc: 0.8090\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f11a5700198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xu6BNOTCmg7W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "76ca4309-638a-4ecc-cc2e-386f42a86735"
      },
      "source": [
        "#Evaluate the model\n",
        "model.evaluate(test_images, test_labels)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 33us/sample - loss: 0.7432 - acc: 0.7822\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.7431911117553711, 0.7822]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uA3ql76DmuB_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "2ba264cb-1904-4b06-b901-cb29bec40bed"
      },
      "source": [
        "#Make a prediction\n",
        "predictions = model.predict(test_images[:5])\n",
        "#Print the predicted labels\n",
        "print(np.argmax(predictions, axis=1))\n",
        "#Print the actual labels\n",
        "print(test_labels[:5])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[9 2 1 1 6]\n",
            "[9 2 1 1 6]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NGpDoFP6wQi2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "61a1978a-d82a-465e-cce3-b98a19fa6e61"
      },
      "source": [
        "for i in range(0,5):\n",
        "  first_image = test_images[i]\n",
        "  first_image = np.array(first_image, dtype='float')\n",
        "  pixels = first_image.reshape((28, 28))\n",
        "  plt.imshow(pixels, cmap='gray')\n",
        "  plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9N/vDiRZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAD1lJREFUeJzt3X+MVfWZx/HPw48B+SGBZRnRkqWg\nrhjUqZlM0E423XTbWFODxMQUk8oaU/pHG7dJE9e4fyz/bGI223b9Y9NkupKi6UI3KUTij6ZKTKDB\nNCBhkVZbAUHAkVFAGX7D8OwfczSDzPme6z333nOH5/1KJnPnPPfMfbzymXPv/Z7z/Zq7C0A846pu\nAEA1CD8QFOEHgiL8QFCEHwiK8ANBEX4gKMIPBEX4gaAmtPLBzIzTCeswffr0ZH3OnDm5tTNnziT3\nnTAh/U/g3Llzyfr48ePrrhedXTpp0qRkfe/evcl6VO5utdyvVPjN7B5JT0saL+m/3f2pMr/vamWW\n/n9RFIKenp5k/bHHHsut7dy5M7nvddddl6zv2bMnWZ82bVqyPnPmzNzahQsXkvsuWLAgWV+2bFmy\njrS6X/ab2XhJ/yXpW5JulbTczG5tVGMAmqvMe/4eSXvcfZ+7n5e0TtLSxrQFoNnKhP8GSQdH/Hwo\n23YZM1tpZtvNbHuJxwLQYE3/wM/d+yT1SXzgB7STMkf+w5Lmjfj5S9k2AGNAmfBvk3STmX3ZzDok\nfUfSxsa0BaDZrMxMPmZ2r6T/1PBQ32p3/7eC+4d82T9uXPpv7KVLl5L1LVu2JOu9vb1fuKdanThx\nIlmfMmVKsp46j+D06dOlfvd9992XrL/wwgvJ+tWqJeP87v6SpJfK/A4A1eD0XiAowg8ERfiBoAg/\nEBThB4Ii/EBQLb2eP6qicfwiXV1dyfqxY8dyax999FFy3zLj9JJ09OjRZP3ixYu5taJLnW+88cZk\n/ZZbbknWo47z14ojPxAU4QeCIvxAUIQfCIrwA0ERfiAohvrGgKIZclPDeddee21y36LLjctO3Z2a\nfrvodxeZN29e8Z2QiyM/EBThB4Ii/EBQhB8IivADQRF+ICjCDwTFOH8b6OzsLLV/arXboqnZi8b5\ni8bxU5fsSunLmYt6K5o2PLU0OYpx5AeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoEqN85vZfkmDkoYk\nXXT37kY0Fc3ixYtL7Z8a57/mmmuS+w4NDZWqF50nkFJ0DkHR9f6zZ8+u+7HRmJN8/t7d05PDA2g7\nvOwHgiobfpf0OzN7w8xWNqIhAK1R9mV/r7sfNrM5kl4xs7fdffPIO2R/FPjDALSZUkd+dz+cfR+Q\ntEFSzyj36XP3bj4MBNpL3eE3s6lmNv3T25K+KWl3oxoD0FxlXvZ3StqQrbQ6QdL/uPtvG9IVgKar\nO/zuvk/SHQ3sJazbb789WT9//nyyfvbs2dxa0RLcqXn1peJ5/1PLgxcpWqK7qLdTp07V/dhgqA8I\ni/ADQRF+ICjCDwRF+IGgCD8QFFN3t4GenitOjLxMavprKT2cVzS19owZM5L1HTt2JOtdXV3J+vHj\nx3NrRZfsFg1THjx4MFlHGkd+ICjCDwRF+IGgCD8QFOEHgiL8QFCEHwiKcf42sGjRomQ9NTW3lD4P\nYNq0acl9+/v7k/UlS5Yk62WWAC+a9nvChPQ/zzKXE4MjPxAW4QeCIvxAUIQfCIrwA0ERfiAowg8E\nxTh/Gyi6pr7omvwy4/zr169P1stKLcNdtPx3kY6OjlL7R8eRHwiK8ANBEX4gKMIPBEX4gaAIPxAU\n4QeCKhznN7PVkr4tacDdF2fbZkn6taT5kvZLetDd8ydoR9KcOXOS9dOnTyfrRdfUp6xdu7bufaXi\nufdnzZqVWzt69Gipxy6a1x9ptRz5fynpns9te0LSJne/SdKm7GcAY0hh+N19s6TPT5myVNKa7PYa\nSfc3uC8ATVbve/5Od/90/qcPJHU2qB8ALVL63H53dzPLfdNpZislrSz7OAAaq94j/xEzmytJ2feB\nvDu6e5+7d7t7d52PBaAJ6g3/RkkrstsrJD3fmHYAtEph+M1sraTXJf2tmR0ys0clPSXpG2b2jqR/\nyH4GMIYUvud39+U5pa83uJewisarT548mawXzW+f8tprr9W9ryS9/vrryfpdd92VW0td61+LsucJ\nRMcZfkBQhB8IivADQRF+ICjCDwRF+IGgmLr7KjBx4sTcWtG030WX5BbZv39/st7b25tbM7NSj/3J\nJ5+U2j86jvxAUIQfCIrwA0ERfiAowg8ERfiBoAg/EBTj/GNA0dTcqXH+vXv3Nrqdyxw6dChZHzcu\n//hSZspxlMeRHwiK8ANBEX4gKMIPBEX4gaAIPxAU4QeCYpx/DLhw4UKyPnXq1Nza7t27G93OZV58\n8cVk/fHHH8+tpc4BQPPx7ANBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUIXj/Ga2WtK3JQ24++Js2ypJ\n35P0YXa3J939pWY1GV2ZpazffffdBnZypV27diXrHR0dubXUPAS1OHXqVKn9o6vlyP9LSfeMsv1n\n7t6VfRF8YIwpDL+7b5Z0rAW9AGihMu/5f2hmu8xstZnNbFhHAFqi3vD/XNJCSV2S+iX9JO+OZrbS\nzLab2fY6HwtAE9QVfnc/4u5D7n5J0i8k9STu2+fu3e7eXW+TABqvrvCb2dwRPy6T1NxLxwA0XC1D\nfWslfU3SbDM7JOlfJX3NzLokuaT9kr7fxB4BNEFh+N19+Sibn2lCL2EVzX0/ZcqUZD01//37779f\nV0+1unjxYt37ljl/QWKcvyzO8AOCIvxAUIQfCIrwA0ERfiAowg8ExdTdbeDIkSPJ+sKFC5P11JDZ\nzTffXFdPtTp//nzd+w4NDZV67KIhUKRx5AeCIvxAUIQfCIrwA0ERfiAowg8ERfiBoBjnbwPbtm1L\n1hctWpSsnzt3Lrd2xx131NVTK0yaNKnU/qn/bhTjyA8ERfiBoAg/EBThB4Ii/EBQhB8IivADQTHO\n3wY2b96crD/yyCPJ+oULF3Jrd955Z109NUrqmv2yU3eXnQ8gOo78QFCEHwiK8ANBEX4gKMIPBEX4\ngaAIPxBU4Ti/mc2T9KykTkkuqc/dnzazWZJ+LWm+pP2SHnT3481r9eq1devWZP3s2bPJemqZ7IGB\ngbp6apTBwcHcmpmV+t1lzxOIrpYj/0VJP3b3WyUtkfQDM7tV0hOSNrn7TZI2ZT8DGCMKw+/u/e6+\nI7s9KOktSTdIWippTXa3NZLub1aTABrvC73nN7P5kr4i6Q+SOt29Pyt9oOG3BQDGiJrP7TezaZJ+\nI+lH7n5i5Ps1d3cz85z9VkpaWbZRAI1V05HfzCZqOPi/cvf12eYjZjY3q8+VNOonS+7e5+7d7t7d\niIYBNEZh+G34EP+MpLfc/acjShslrchur5D0fOPbA9Astbzs/6qk70p608x2ZtuelPSUpP81s0cl\nHZD0YHNavPodOHAgWT9x4kSynpoCe/Lkycl9FyxYkKzv27cvWS+Sutx4woRyV5Qz1FdO4bPv7r+X\nlDcg+/XGtgOgVTjDDwiK8ANBEX4gKMIPBEX4gaAIPxAUU3ePAUVLWafGuzs6OpL7Nnucv7+/P7c2\nf/785L7Hjh1L1seN49hVBs8eEBThB4Ii/EBQhB8IivADQRF+ICjCDwTFOH8LFE1R7T7qDGif2bBh\nQ7L+0EMP5daKxsJ7e3uT9VdffTVZL3Lq1Km69y163j7++OO6fzc48gNhEX4gKMIPBEX4gaAIPxAU\n4QeCIvxAUIzzt0DZcf7nn0+vh/Lwww/n1lLz5kvSAw88kKyvWrUqWS+Smpu/6L+7qF60dDnSOPID\nQRF+ICjCDwRF+IGgCD8QFOEHgiL8QFCF4/xmNk/Ss5I6JbmkPnd/2sxWSfqepA+zuz7p7i81q9Gx\nrOia+kuXLiXrL7/8crJ+/Pjx3FrRnP9Fj13W7t27c2u33XZbct8zZ84k69dff31dPWFYLSf5XJT0\nY3ffYWbTJb1hZq9ktZ+5+380rz0AzVIYfnfvl9Sf3R40s7ck3dDsxgA01xd6z29m8yV9RdIfsk0/\nNLNdZrbazGbm7LPSzLab2fZSnQJoqJrDb2bTJP1G0o/c/YSkn0taKKlLw68MfjLafu7e5+7d7t7d\ngH4BNEhN4TeziRoO/q/cfb0kufsRdx9y90uSfiGpp3ltAmi0wvDb8CVpz0h6y91/OmL73BF3WyYp\n/2NdAG2nlk/7vyrpu5LeNLOd2bYnJS03sy4ND//tl/T9pnR4FRgaGmrq73/vvfdya0uWLEnuO3Xq\n1GT97rvvTta3bt2arKeWD588eXJy34kTJybrs2fPTtaRVsun/b+XNNoF6YzpA2MYZ/gBQRF+ICjC\nDwRF+IGgCD8QFOEHgmLq7hYomoK6rL6+vtza22+/ndx33bp1yXrROH6R5557Lrc2Y8aM5L6Dg4PJ\n+pYtW+rqCcM48gNBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUNbsMejLHszsQ0kHRmyaLemjljXwxbRr\nb+3al0Rv9Wpkb3/j7n9dyx1bGv4rHtxse7vO7deuvbVrXxK91auq3njZDwRF+IGgqg5//knp1WvX\n3tq1L4ne6lVJb5W+5wdQnaqP/AAqUkn4zeweM/uzme0xsyeq6CGPme03szfNbGfVS4xly6ANmNnu\nEdtmmdkrZvZO9n3UZdIq6m2VmR3OnrudZnZvRb3NM7PXzOxPZvZHM/unbHulz12ir0qet5a/7Dez\n8ZL+Iukbkg5J2iZpubv/qaWN5DCz/ZK63b3yMWEz+ztJJyU96+6Ls23/LumYuz+V/eGc6e7/3Ca9\nrZJ0suqVm7MFZeaOXFla0v2S/lEVPneJvh5UBc9bFUf+Hkl73H2fu5+XtE7S0gr6aHvuvlnSsc9t\nXippTXZ7jYb/8bRcTm9twd373X1HdntQ0qcrS1f63CX6qkQV4b9B0sERPx9Sey357ZJ+Z2ZvmNnK\nqpsZRWe2bLokfSCps8pmRlG4cnMrfW5l6bZ57upZ8brR+MDvSr3ufqekb0n6Qfbyti358Hu2dhqu\nqWnl5lYZZWXpz1T53NW74nWjVRH+w5Lmjfj5S9m2tuDuh7PvA5I2qP1WHz7y6SKp2feBivv5TDut\n3DzaytJqg+eunVa8riL82yTdZGZfNrMOSd+RtLGCPq5gZlOzD2JkZlMlfVPtt/rwRkkrstsrJD1f\nYS+XaZeVm/NWllbFz13brXjt7i3/knSvhj/x3yvpX6roIaevBZL+L/v6Y9W9SVqr4ZeBFzT82cij\nkv5K0iZJ70h6VdKsNurtOUlvStql4aDNrai3Xg2/pN8laWf2dW/Vz12ir0qeN87wA4LiAz8gKMIP\nBEX4gaAIPxAU4QeCIvxAUIQfCIrwA0H9P8T/1XYD/jtgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}